The field of the invention relates generally to demand response systems and, more particularly, to a computing device for use with a demand response system that enables utilities to predict a reduction of energy consumption by their customers.
As the human population increases around the world and with an increase in the use of electric vehicles by customers, energy demand will also likely increase. More specifically, energy demand will likely increase in the form of electrical energy used to power buildings, homes, and/or to charge batteries or other energy sources used in electric vehicles. Moreover, the demand on the power grid is likely to increase while the demand for fuel decreases. Such demands will likely cause an increase in the price of energy from the power grid. In particular, the price of energy is likely to increase during peak times, such as a time of day and/or a day of the week, when demand for energy is high.
Currently, at least some known utilities use demand response systems that enable customers to enroll in at least one demand response program to manage the consumption of energy by their customers in response to supply conditions. Examples of demand response programs include a direct control program, a peak pricing program, such as a critical peak pricing program, and a time of use program. The initiation and/or implementation of a demand response program by a utility is known as a demand response event. A demand response event is initiated by a utility transmitting a plurality of signals to its customers. For example, a demand response event representative of a direct load control program, is initiated when the utility transmits a signal to a device within a building, such as an in-home smart device and/or smart thermostat, such that the utility is enabled to directly control the usage of energy consuming machines within the building. A demand response event representative of a critical peak pricing program occurs when the utility transmits pricing signals to its customers during peak demand times. The pricing signals enable the utility to apprise customers of heightened energy prices during peak demand time periods such that customers may limit their energy consumption during such peak demand time periods. A demand response event representative of a time of use program occurs when the utility transmits a signal to a customer that is representative of energy prices that correspond to a time range such that the customer may identify an optimal time of day and/or day of the week to consume energy to ensure a low energy price rate.
Such demand response systems enable the utility to manage peak load conditions and to reduce energy demand among its customers. More specifically, utilities have customers enroll in demand response programs to manage peak load conditions by having each customer receive a fixed number of demand response events per day, week, and/or month. However, current demand response systems are not configured to enable a utility to monitor the reduction in energy consumption by customers in order to accurately predict the future reduction of energy consumption by each customer based on demand response events that each customer may participate in. An accurate estimate for a potential load reduction that is based on implementing demand response programs is critical information for a utility to have in managing demand response events. Utilities may endure detrimental economic implications if the reduction of energy consumption caused by a demand response event is greater than or less than expected. For example, if estimates of a reduction in energy consumption by customers are not substantially accurate, then utilities may not schedule enough demand response events for their customers. Alternatively, utilities may schedule too many events by transmitting signals to all their customers, even the customers who may not necessarily participate in an event. Both aforementioned scenarios may cause a utility to lose revenue. Customers may also be upset when there is an overutilization and/or underutilization of demand response events.